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Posts Tagged ‘mutual fund risk’

Further Analysis of the Laudus Rosenberg Fund

June 21st, 2010

Michael Markov and Kushal Kshirsagar

In our previous post below—referenced in a story from Jeff Sommer of The New York Times—we performed an analysis of the Laudus Rosenberg US Large Cap mutual fund (sub-advised by AXA Rosenberg) that indicated a significant change in the fund’s risk profile occurred as early as 2008. Our study showed a substantial increase in the daily tracking error of the fund to its benchmark – the Russell 1000 Index. The tracking error reached its peak in June 2009 and was several times higher than many of its quant Large Cap Core peers. So there was a symptom that could have alerted risk managers or investors to potential problems. Note that this symptom became apparent only on shorter-horizon “daily temperature charts” rather than two-year averages commonly used by many practitioners.

We then performed some quick diagnostics to understand the nature of the problem and to eliminate false alarms such as data issues or a specific stock bet. We found none of those but the fund’s daily beta was on a path of steep descent in 2009— another worrying symptom but no diagnosis yet.

To get a better understanding of the nature of the problem we will perform a full multi-factor “metabolic panel” of the fund—daily returns-bases style analysis (RBSA). When performed with the right tools that can filter the daily noise, such an analysis could provide key insights and potential answers to specific questions. Through this daily returns-based analysis, we seek to learn more about attribution of the fund’s gains or losses: what bets were made, when were they made and for how long – and with what impact on both performance and risk? It is important to remember that this analysis does not look at actual holdings. Instead, our analysis seeks to understand a fund’s return behavior by comparing its daily returns to those of various passive indices.

Using daily fund NAVs we performed returns-based style analysis in MPI’s Stylus Pro software of Laudus Rosenberg US Large Cap funds against the S&P 500 sectors. The results of the analysis are presented in the charts below where color bands represent portfolio exposures to S&P sectors and cash (exposure to US Treasury bills).

The change in sector exposure at the end of 2008 is immediately apparent with some significant shifts both before and after. Exposure to Energy, Health Care and Consumer Staples changed in a profound way. The quality of this analysis is very high with R-squared being in the 99% range. Again, please note that the analysis was performed using only fund’s NAV and without use of any holdings information and such a result may not directly imply that the fund indeed had such sector position at the time. The best way to further identify possible bets is to plot the fund’s exposures over the S&P 500 index.

In the chart, the bands above the zero line denote sectors where the fund’s sector exposures are greater than those of the S&P 500 index and the ones lower than zero indicate sector exposures less than the index. In 2009, fund exposure differentials peaked in the 2nd QTR and then started slowly normalizing by the end of the year. Our returns-based analysis shows that the fund may have been significantly overexposed to Health Care (the lowest beta sector) and underexposed to some of the highest beta sectors such as Financials. The increased cash position (green color) may indicate that the fund selected more defensive (i.e. lower beta) stocks within sectors. All of these factors could have contributed to the fund having very low beta behavior in 2009, as identified in our previous post.

Incidentally, this period was characterized by what many referred to as a “dash for trash”. Lower quality stocks that were priced for an Armageddon scenario rebounded strongly when the cycle turned in the second quarter of 2009. Consequently, higher quality names, in general, underperformed these lower quality stocks resulting in the underperformance of many fundamental (both quantitative and non-quantitative) alpha strategies. The impact of this phenomenon on a fund’s performance depended on the extent to which these alpha bets were reined in by risk controls.

And finally, let’s see what RBSA can tell us about fund liquidation. On May 2, the board of directors of Charles Schwab made a decision to liquidate four Laudus Rosenberg funds. The funds were immediately closed to new investors and the liquidation was scheduled for July 30th. The fund is still reporting daily NAVs which make it possible to understand how the liquidation is progressing in terms of the fund’s exposure to major sectors. The chart below, showing daily returns-based exposures of the US Large Cap fund , indicates that the fund is currently behaving as though it is about 65% cash with exposure to just a handful of other sectors.

Having such little exposure to the market over the past several weeks clearly boosted the fund’s performance for remaining investors! Again, we would like to make a disclaimer that this analysis is based only on the fund’s NAVs and may not reflect the fund’s actual positions.

Michael Markov Main, Mutual Funds, Research , , , , , , , , ,

AXA Rosenberg: Daily Data Proves Crucial in Risk Monitoring

May 14th, 2010

Michael Markov and Kushal Kshirsagar

With AXA Rosenberg’s recent admission of a 2009 coding error in its portfolio risk management programs during a year in which the firm’s equity mutual funds lagged their benchmarks by a large margin, the investment community must reflect on how we monitor investments. Specifically, investors (and their advisers) must ask if we have the right tools and protocols to identify unusual risk and performance patterns in a timely manner.

According to the company’s statement in an April 15th letter to investors, the error was discovered in June 2009 and fixed between September and mid-November. Because the “coding error” apparently impacted risk controls, we examined two basic risk measures that are routinely used by both fund managers and investors to evaluate and monitor investment products: Beta and Tracking Error. The chart below shows Beta of the Laudus Rosenberg US Large Capitalization Fund in 2009 with a sample of its peers computed using daily fund NAVs1.

The fund’s Beta appears to be very different from a sample of other quantitative large cap US equity funds2 –the Laudus Rosenberg fund had a sharp drop in market beta right from the start of 2009. It’s plausible that having a beta substantially below 1 during the market rally in March-June of 2009 may have impacted the fund’s performance. What’s intriguing is that another risk measure—the 3-month rolling tracking error to Russell 1000 (as shown in chart below) was unusually high throughout the entire 2009 and was several times higher than that of any of the other quant managers in the group.

Using daily data to monitor Laudus Rosenberg’s beta and tracking error, could we have raised a red flag in June 2009?

It is worth stressing that such an apparent aberration in the fund’s risk profile could be most clearly seen using daily data. Unfortunately, investors typically use monthly data even though daily returns are now easily available from data providers (e.g. Lipper), public sources (Yahoo, Google, etc.), funds and custodians. When using monthly data, a longer history needs to be used to have sufficient observations to estimate the regression. This longer history may cloud, or, as in the case of the Laudus Rosenberg fund, completely transform the picture. The chart below uses a rolling 24 month window to calculate the fund’s beta.

When used in more granular returns-based analysis (RBSA), daily fund NAVs could provide answers to specific questions about attribution of the fund’s gains or losses: what bets were made, when and for how long and with what impact on both performance and risk. Thus, in one of our recent research papers on Oppenheimer Core Bond Fund, we demonstrated the importance of daily data in analysis and monitoring leverage of complex fixed-income portfolios having derivative exposure.

Related links:
http://online.wsj.com/article/SB10001424052748704388304575202501743719416.html
http://www.pionline.com/article/20100419/PRINTSUB/304199980
http://www.pionline.com/article/20100518/DAILYREG/100519849
http://news.morningstar.com/articlenet/article.aspx?id=335669
http://online.wsj.com/article/BT-CO-20100510-714684.html

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  1. The daily fund beta is calculated vs. Russell 1000 Index using rolling 66-day (approximately 3 calendar months) calculations. Russell 1000 is the stated benchmark of the Laudus fund. []
  2. Details on the other quant equity funds used in the study:
    1. The Vanguard Growth & Income fund is sub-advised by Mellon and uses the S&P 500 as a benchmark.
    2. The Russell US Quant Equity fund is a fund of quantitative funds and currently has 6 sub-advisers: Aronson + Johnson + Ortiz, Goldman Sachs Asset Management, INTECH, Jacobs Levy, Numeric and Russell Investment Management Company
    3. The Vanguard Structured Large-Cap Equity Fund (VSLIX) employs a quantitative strategy and is managed by the Vanguard Quantitative Equity Group. Benchmark: S&P 500 Index
    4. The Goldman Sachs Structured US Equity fund is managed by Goldman Sachs Asset Management and uses the S&P 500 as its benchmark.
    []

Michael Markov Main, Mutual Funds , , , , , , , , , , , , , ,

Identifying Bond Fund Risks Before Getting Burned

June 23rd, 2009

The class action lawsuit involving the Oppenheimer Core Bond Fund (OPIGX) alleges that the firm understated the fund’s risks as reported in a Read more…

Michael Markov Main, Mutual Funds, Research , , , ,